Bio
M. Ángeles Serrano obtained her Ph.D. in Physics at the Universitat de Barcelona (UB) in 1999 with a thesis about gravitational wave detection. In 2000, she also received her Masters in Mathematics for Finance at the CRM-Universitat Autònoma de Barcelona. After four years in the private sector as IT consultant and mutual funds manager, Prof. Serrano returned to academia in 2004 to work in the field of Network Science. Subsequently, she was a researcher at Indiana University (USA), the École Polytechnique Fédérale de Lausanne (Switzerland), IFISC Institute (Spain), and held a Ramón y Cajal research associate appointment at UB until october 2015. The results of her investigations are summarized in major peer reviewed international scientific journals -including Nature, PNAS, PRL, …-, book chapters, and conference proceedings. Prof. Serrano leads and participates in several research projects at the international and national levels. She is also actively involved in advising and research supervision. She serves in evaluation panels and program scientific committees, and acts as a reviewer in several international journals. In February 2009, she obtained the Outstanding Referee Award of the American Physical Society. She is a Founder Member of Complexitat, the Catalan Network for the study of Complex Systems, and Promoter and Board Member of UBICS, the Universitat de Barcelona Institute of Complex Systems.
Talks
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Abstract
Scaling up and down complex networks
Invited Talks
02-10-2022 - 10:00-10:30
The renormalization group allows a systematic investigation of physical systems when observed at different length scales. However, the small-world property of complex networks complicates renormalization by introducing correlations between coexisting scales. Network geometry offers now a powerful framework to renormalize real complex networks on the basis of similarity distances between nodes in a latent hyperbolic space. The technique is based on network maps, geometric representations of real networks that are progressively coarse-grained and rescaled to unfold them into a multilayer shell. We found that the renormalized multilayer shells of real networks, including connectomes of the human brain, show multiscale self-similarity, which is also found in the evolution of some growing real networks. This suggests that evolutionary processes can be modeled by reversing renormalization. Multiscale network shells can be used to produce scaled down and scaled up replicas of real networks, a useful tool for the study of processes where network size is relevant.
